Overview

Brought to you by YData

Dataset statistics

Number of variables26
Number of observations231
Missing cells874
Missing cells (%)14.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory193.1 KiB
Average record size in memory856.2 B

Variable types

Text2
DateTime1
Categorical15
Numeric8

Alerts

Cluster_6 has constant value "1" Constant
Estado is highly overall correlated with cant_MontoLimiteHigh correlation
anio_preinscripcion is highly overall correlated with antiguedad and 1 other fieldsHigh correlation
antiguedad is highly overall correlated with anio_preinscripcion and 1 other fieldsHigh correlation
cant_Apoderado is highly overall correlated with cant_MontoLimite and 2 other fieldsHigh correlation
cant_MontoLimite is highly overall correlated with Estado and 9 other fieldsHigh correlation
cant_antecedentes is highly overall correlated with cant_MontoLimite and 1 other fieldsHigh correlation
cant_apercibimientos is highly overall correlated with cant_MontoLimite and 1 other fieldsHigh correlation
cant_autenticado is highly overall correlated with cant_representante and 1 other fieldsHigh correlation
cant_noAutenticado is highly overall correlated with cant_Apoderado and 3 other fieldsHigh correlation
cant_procesos_adjudicado is highly overall correlated with monto_total_adjudicadoHigh correlation
cant_representante is highly overall correlated with cant_MontoLimite and 1 other fieldsHigh correlation
cant_sinMontoLimite is highly overall correlated with cant_Apoderado and 2 other fieldsHigh correlation
cant_suspensiones is highly overall correlated with cant_MontoLimite and 1 other fieldsHigh correlation
dcant_procesos_adjudicado is highly overall correlated with cant_MontoLimiteHigh correlation
dmonto_total_adjudicado is highly overall correlated with cant_MontoLimiteHigh correlation
dtotal_articulos_provee is highly overall correlated with cant_noAutenticadoHigh correlation
monto_total_adjudicado is highly overall correlated with cant_MontoLimite and 1 other fieldsHigh correlation
periodo_preinscripcion is highly overall correlated with anio_preinscripcion and 1 other fieldsHigh correlation
total_articulos_provee is highly overall correlated with cant_MontoLimiteHigh correlation
Estado is highly imbalanced (67.0%) Imbalance
provincia is highly imbalanced (53.1%) Imbalance
cant_apercibimientos is highly imbalanced (74.5%) Imbalance
cant_Apoderado is highly imbalanced (61.2%) Imbalance
cant_representante is highly imbalanced (60.9%) Imbalance
cant_autenticado is highly imbalanced (66.5%) Imbalance
cant_noAutenticado is highly imbalanced (54.7%) Imbalance
cant_socios has 55 (23.8%) missing values Missing
cant_apercibimientos has 91 (39.4%) missing values Missing
cant_suspensiones has 117 (50.6%) missing values Missing
cant_antecedentes has 7 (3.0%) missing values Missing
cant_Apoderado has 66 (28.6%) missing values Missing
cant_representante has 120 (51.9%) missing values Missing
cant_noAutenticado has 186 (80.5%) missing values Missing
cant_MontoLimite has 225 (97.4%) missing values Missing
CUIT has unique values Unique

Reproduction

Analysis started2025-06-30 15:01:35.304413
Analysis finished2025-06-30 15:01:41.529085
Duration6.22 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

CUIT
Text

Unique 

Distinct231
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size17.1 KiB
2025-06-30T12:01:41.667532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.995671
Min length9

Characters and Unicode

Total characters2540
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique231 ?
Unique (%)100.0%

Sample

1st row30711500363
2nd row30590151013
3rd row30678561165
4th row30591267759
5th row30702024834
ValueCountFrequency (%)
30711500363 1
 
0.4%
30590151013 1
 
0.4%
30678561165 1
 
0.4%
30591267759 1
 
0.4%
30702024834 1
 
0.4%
30623295946 1
 
0.4%
30673249902 1
 
0.4%
30714236888 1
 
0.4%
30710362218 1
 
0.4%
30710828608 1
 
0.4%
Other values (221) 221
95.7%
2025-06-30T12:01:41.866440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 430
16.9%
3 353
13.9%
7 287
11.3%
2 272
10.7%
1 235
9.3%
6 217
8.5%
9 197
7.8%
5 193
7.6%
8 186
7.3%
4 169
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2540
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 430
16.9%
3 353
13.9%
7 287
11.3%
2 272
10.7%
1 235
9.3%
6 217
8.5%
9 197
7.8%
5 193
7.6%
8 186
7.3%
4 169
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2540
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 430
16.9%
3 353
13.9%
7 287
11.3%
2 272
10.7%
1 235
9.3%
6 217
8.5%
9 197
7.8%
5 193
7.6%
8 186
7.3%
4 169
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2540
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 430
16.9%
3 353
13.9%
7 287
11.3%
2 272
10.7%
1 235
9.3%
6 217
8.5%
9 197
7.8%
5 193
7.6%
8 186
7.3%
4 169
 
6.7%

Nombre
Text

Distinct226
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size19.4 KiB
2025-06-30T12:01:42.007113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length67
Median length34
Mean length18.549784
Min length3

Characters and Unicode

Total characters4285
Distinct characters65
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique225 ?
Unique (%)97.4%

Sample

1st rowLICICOM S.R.L.
2nd rowVIDITEC S.A..
3rd rowNACION SEGUROS S.A.
4th rowERRE-DE SRL
5th rowDatastar Argentina S.A.
ValueCountFrequency (%)
s.a 62
 
9.4%
srl 39
 
5.9%
sa 27
 
4.1%
s.r.l 24
 
3.6%
argentina 15
 
2.3%
y 12
 
1.8%
de 11
 
1.7%
sin 6
 
0.9%
datos 6
 
0.9%
a 6
 
0.9%
Other values (401) 451
68.4%
2025-06-30T12:01:42.253463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
428
 
10.0%
A 357
 
8.3%
S 313
 
7.3%
R 270
 
6.3%
E 241
 
5.6%
I 236
 
5.5%
. 207
 
4.8%
O 177
 
4.1%
N 171
 
4.0%
L 158
 
3.7%
Other values (55) 1727
40.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4285
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
428
 
10.0%
A 357
 
8.3%
S 313
 
7.3%
R 270
 
6.3%
E 241
 
5.6%
I 236
 
5.5%
. 207
 
4.8%
O 177
 
4.1%
N 171
 
4.0%
L 158
 
3.7%
Other values (55) 1727
40.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4285
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
428
 
10.0%
A 357
 
8.3%
S 313
 
7.3%
R 270
 
6.3%
E 241
 
5.6%
I 236
 
5.5%
. 207
 
4.8%
O 177
 
4.1%
N 171
 
4.0%
L 158
 
3.7%
Other values (55) 1727
40.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4285
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
428
 
10.0%
A 357
 
8.3%
S 313
 
7.3%
R 270
 
6.3%
E 241
 
5.6%
I 236
 
5.5%
. 207
 
4.8%
O 177
 
4.1%
N 171
 
4.0%
L 158
 
3.7%
Other values (55) 1727
40.3%
Distinct173
Distinct (%)74.9%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
Minimum2016-02-08 00:00:00
Maximum2022-02-20 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-06-30T12:01:42.347198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T12:01:42.456811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Estado
Categorical

High correlation  Imbalance 

Distinct8
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
Inscripto
196 
Desactualizado doc. vencidos
 
11
Suspendido
 
6
Con Solicitud De Baja
 
6
Desactualizado mantención
 
6
Other values (3)
 
6

Length

Max length28
Median length9
Mean length10.809524
Min length9

Characters and Unicode

Total characters2497
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.9%

Sample

1st rowInscripto
2nd rowInscripto
3rd rowInscripto
4th rowInscripto
5th rowInscripto

Common Values

ValueCountFrequency (%)
Inscripto 196
84.8%
Desactualizado doc. vencidos 11
 
4.8%
Suspendido 6
 
2.6%
Con Solicitud De Baja 6
 
2.6%
Desactualizado mantención 6
 
2.6%
Pre Inscripto 4
 
1.7%
En Evaluacion 1
 
0.4%
Desactualizado Por Clase 1
 
0.4%

Length

2025-06-30T12:01:42.568665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T12:01:42.641264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
inscripto 200
70.4%
desactualizado 18
 
6.3%
doc 11
 
3.9%
vencidos 11
 
3.9%
suspendido 6
 
2.1%
con 6
 
2.1%
solicitud 6
 
2.1%
de 6
 
2.1%
baja 6
 
2.1%
mantención 6
 
2.1%
Other values (5) 8
 
2.8%

Most occurring characters

ValueCountFrequency (%)
o 260
10.4%
i 254
10.2%
c 253
10.1%
n 243
9.7%
s 236
9.5%
t 230
9.2%
p 206
8.2%
r 205
8.2%
I 200
8.0%
a 75
 
3.0%
Other values (17) 335
13.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2497
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 260
10.4%
i 254
10.2%
c 253
10.1%
n 243
9.7%
s 236
9.5%
t 230
9.2%
p 206
8.2%
r 205
8.2%
I 200
8.0%
a 75
 
3.0%
Other values (17) 335
13.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2497
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 260
10.4%
i 254
10.2%
c 253
10.1%
n 243
9.7%
s 236
9.5%
t 230
9.2%
p 206
8.2%
r 205
8.2%
I 200
8.0%
a 75
 
3.0%
Other values (17) 335
13.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2497
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 260
10.4%
i 254
10.2%
c 253
10.1%
n 243
9.7%
s 236
9.5%
t 230
9.2%
p 206
8.2%
r 205
8.2%
I 200
8.0%
a 75
 
3.0%
Other values (17) 335
13.4%

TipoSocietario
Categorical

Distinct8
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size23.5 KiB
Sociedad Anónima
95 
S.R.L
67 
Persona Física
54 
Otras Formas Societarias
 
7
Sociedades De Hecho
 
3
Other values (3)
 
5

Length

Max length26
Median length24
Mean length12.74026
Min length5

Characters and Unicode

Total characters2943
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.9%

Sample

1st rowS.R.L
2nd rowSociedad Anónima
3rd rowSociedad Anónima
4th rowS.R.L
5th rowSociedad Anónima

Common Values

ValueCountFrequency (%)
Sociedad Anónima 95
41.1%
S.R.L 67
29.0%
Persona Física 54
23.4%
Otras Formas Societarias 7
 
3.0%
Sociedades De Hecho 3
 
1.3%
PJ Extranjero Sin Sucursal 3
 
1.3%
Cooperativas 1
 
0.4%
Organismo Publico 1
 
0.4%

Length

2025-06-30T12:01:42.735012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T12:01:42.797499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sociedad 95
23.2%
anónima 95
23.2%
s.r.l 67
16.3%
persona 54
13.2%
física 54
13.2%
otras 7
 
1.7%
formas 7
 
1.7%
societarias 7
 
1.7%
sociedades 3
 
0.7%
de 3
 
0.7%
Other values (8) 18
 
4.4%

Most occurring characters

ValueCountFrequency (%)
a 338
 
11.5%
i 267
 
9.1%
n 251
 
8.5%
d 196
 
6.7%
179
 
6.1%
S 178
 
6.0%
o 176
 
6.0%
e 172
 
5.8%
c 166
 
5.6%
s 137
 
4.7%
Other values (26) 883
30.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2943
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 338
 
11.5%
i 267
 
9.1%
n 251
 
8.5%
d 196
 
6.7%
179
 
6.1%
S 178
 
6.0%
o 176
 
6.0%
e 172
 
5.8%
c 166
 
5.6%
s 137
 
4.7%
Other values (26) 883
30.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2943
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 338
 
11.5%
i 267
 
9.1%
n 251
 
8.5%
d 196
 
6.7%
179
 
6.1%
S 178
 
6.0%
o 176
 
6.0%
e 172
 
5.8%
c 166
 
5.6%
s 137
 
4.7%
Other values (26) 883
30.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2943
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 338
 
11.5%
i 267
 
9.1%
n 251
 
8.5%
d 196
 
6.7%
179
 
6.1%
S 178
 
6.0%
o 176
 
6.0%
e 172
 
5.8%
c 166
 
5.6%
s 137
 
4.7%
Other values (26) 883
30.0%

periodo_preinscripcion
Real number (ℝ)

High correlation 

Distinct43
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201694.48
Minimum201607
Maximum202202
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2025-06-30T12:01:42.891238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum201607
5-th percentile201608
Q1201611
median201701
Q3201707.5
95-th percentile201905
Maximum202202
Range595
Interquartile range (IQR)96.5

Descriptive statistics

Standard deviation97.591644
Coefficient of variation (CV)0.00048385877
Kurtosis4.4879017
Mean201694.48
Median Absolute Deviation (MAD)90
Skewness1.7293275
Sum46591425
Variance9524.129
MonotonicityNot monotonic
2025-06-30T12:01:42.984983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
201611 27
 
11.7%
201701 25
 
10.8%
201610 22
 
9.5%
201612 19
 
8.2%
201609 14
 
6.1%
201703 14
 
6.1%
201702 11
 
4.8%
201704 11
 
4.8%
201608 9
 
3.9%
201705 8
 
3.5%
Other values (33) 71
30.7%
ValueCountFrequency (%)
201607 4
 
1.7%
201608 9
 
3.9%
201609 14
6.1%
201610 22
9.5%
201611 27
11.7%
201612 19
8.2%
201701 25
10.8%
201702 11
4.8%
201703 14
6.1%
201704 11
4.8%
ValueCountFrequency (%)
202202 1
0.4%
202111 1
0.4%
202010 1
0.4%
202009 1
0.4%
202001 1
0.4%
201911 1
0.4%
201910 2
0.9%
201909 1
0.4%
201908 1
0.4%
201907 1
0.4%

anio_preinscripcion
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016.8745
Minimum2016
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2025-06-30T12:01:43.047474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2016
Q12016
median2017
Q32017
95-th percentile2019
Maximum2022
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.98986013
Coefficient of variation (CV)0.00049078916
Kurtosis4.1821911
Mean2016.8745
Median Absolute Deviation (MAD)1
Skewness1.6384554
Sum465898
Variance0.97982308
MonotonicityNot monotonic
2025-06-30T12:01:43.109977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2016 95
41.1%
2017 92
39.8%
2018 30
 
13.0%
2019 9
 
3.9%
2020 3
 
1.3%
2021 1
 
0.4%
2022 1
 
0.4%
ValueCountFrequency (%)
2016 95
41.1%
2017 92
39.8%
2018 30
 
13.0%
2019 9
 
3.9%
2020 3
 
1.3%
2021 1
 
0.4%
2022 1
 
0.4%
ValueCountFrequency (%)
2022 1
 
0.4%
2021 1
 
0.4%
2020 3
 
1.3%
2019 9
 
3.9%
2018 30
 
13.0%
2017 92
39.8%
2016 95
41.1%

cant_procesos_adjudicado
Real number (ℝ)

High correlation 

Distinct111
Distinct (%)48.5%
Missing2
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean100.74672
Minimum1
Maximum1214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2025-06-30T12:01:43.178868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median27
Q382
95-th percentile439.2
Maximum1214
Range1213
Interquartile range (IQR)75

Descriptive statistics

Standard deviation196.61717
Coefficient of variation (CV)1.9515987
Kurtosis12.227338
Mean100.74672
Median Absolute Deviation (MAD)24
Skewness3.3737497
Sum23071
Variance38658.313
MonotonicityNot monotonic
2025-06-30T12:01:43.288239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 18
 
7.8%
1 11
 
4.8%
2 10
 
4.3%
13 8
 
3.5%
5 7
 
3.0%
22 7
 
3.0%
6 5
 
2.2%
8 5
 
2.2%
4 5
 
2.2%
48 4
 
1.7%
Other values (101) 149
64.5%
ValueCountFrequency (%)
1 11
4.8%
2 10
4.3%
3 18
7.8%
4 5
 
2.2%
5 7
 
3.0%
6 5
 
2.2%
7 3
 
1.3%
8 5
 
2.2%
9 1
 
0.4%
10 2
 
0.9%
ValueCountFrequency (%)
1214 1
0.4%
1102 1
0.4%
989 1
0.4%
895 1
0.4%
889 1
0.4%
864 1
0.4%
804 1
0.4%
792 1
0.4%
649 1
0.4%
635 1
0.4%

monto_total_adjudicado
Real number (ℝ)

High correlation 

Distinct229
Distinct (%)100.0%
Missing2
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean2.387313 × 108
Minimum6872
Maximum6.9338191 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2025-06-30T12:01:43.554471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6872
5-th percentile518307.71
Q16445227.6
median33810393
Q31.6714006 × 108
95-th percentile8.2985888 × 108
Maximum6.9338191 × 109
Range6.9338122 × 109
Interquartile range (IQR)1.6069483 × 108

Descriptive statistics

Standard deviation7.2642602 × 108
Coefficient of variation (CV)3.0428604
Kurtosis49.438449
Mean2.387313 × 108
Median Absolute Deviation (MAD)32548404
Skewness6.4804021
Sum5.4669467 × 1010
Variance5.2769476 × 1017
MonotonicityNot monotonic
2025-06-30T12:01:43.652625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7425067.382 1
 
0.4%
6933819091 1
 
0.4%
137988939 1
 
0.4%
3182253860 1
 
0.4%
27146231.22 1
 
0.4%
1375720032 1
 
0.4%
7242920.713 1
 
0.4%
86070142.1 1
 
0.4%
64475958.4 1
 
0.4%
474560333 1
 
0.4%
Other values (219) 219
94.8%
(Missing) 2
 
0.9%
ValueCountFrequency (%)
6872 1
0.4%
23910.30243 1
0.4%
42000 1
0.4%
84943.48 1
0.4%
100200 1
0.4%
112000 1
0.4%
132004.6361 1
0.4%
213056.6512 1
0.4%
240550.6182 1
0.4%
354057.6923 1
0.4%
ValueCountFrequency (%)
6933819091 1
0.4%
5903229493 1
0.4%
3182253860 1
0.4%
3132430594 1
0.4%
2548401882 1
0.4%
2252733473 1
0.4%
2092322557 1
0.4%
1375720032 1
0.4%
1375349531 1
0.4%
943321354.4 1
0.4%

antiguedad
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1298701
Minimum0
Maximum5
Zeros2
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2025-06-30T12:01:43.724351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median4
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.96932046
Coefficient of variation (CV)0.23470967
Kurtosis2.8156944
Mean4.1298701
Median Absolute Deviation (MAD)1
Skewness-1.4480491
Sum954
Variance0.93958216
MonotonicityNot monotonic
2025-06-30T12:01:43.771210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 95
41.1%
4 92
39.8%
3 30
 
13.0%
2 9
 
3.9%
1 3
 
1.3%
0 2
 
0.9%
ValueCountFrequency (%)
0 2
 
0.9%
1 3
 
1.3%
2 9
 
3.9%
3 30
 
13.0%
4 92
39.8%
5 95
41.1%
ValueCountFrequency (%)
5 95
41.1%
4 92
39.8%
3 30
 
13.0%
2 9
 
3.9%
1 3
 
1.3%
0 2
 
0.9%

provincia
Categorical

Imbalance 

Distinct15
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size16.5 KiB
CABA
132 
Buenos Aires
66 
Santa Fe
 
9
Córdoba
 
7
San Juan
 
3
Other values (10)
14 

Length

Max length12
Median length4
Mean length6.8701299
Min length4

Characters and Unicode

Total characters1587
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)3.0%

Sample

1st rowCABA
2nd rowCABA
3rd rowCABA
4th rowBuenos Aires
5th rowCABA

Common Values

ValueCountFrequency (%)
CABA 132
57.1%
Buenos Aires 66
28.6%
Santa Fe 9
 
3.9%
Córdoba 7
 
3.0%
San Juan 3
 
1.3%
Extranjera 3
 
1.3%
San Luis 2
 
0.9%
Corrientes 2
 
0.9%
Rio Negro 1
 
0.4%
La Rioja 1
 
0.4%
Other values (5) 5
 
2.2%

Length

2025-06-30T12:01:43.860871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
caba 132
42.0%
buenos 66
21.0%
aires 66
21.0%
santa 9
 
2.9%
fe 9
 
2.9%
córdoba 7
 
2.2%
san 5
 
1.6%
juan 3
 
1.0%
extranjera 3
 
1.0%
luis 2
 
0.6%
Other values (11) 12
 
3.8%

Most occurring characters

ValueCountFrequency (%)
A 330
20.8%
B 198
12.5%
e 151
9.5%
C 143
9.0%
s 137
8.6%
n 91
 
5.7%
r 86
 
5.4%
83
 
5.2%
o 80
 
5.0%
u 75
 
4.7%
Other values (23) 213
13.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1587
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 330
20.8%
B 198
12.5%
e 151
9.5%
C 143
9.0%
s 137
8.6%
n 91
 
5.7%
r 86
 
5.4%
83
 
5.2%
o 80
 
5.0%
u 75
 
4.7%
Other values (23) 213
13.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1587
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 330
20.8%
B 198
12.5%
e 151
9.5%
C 143
9.0%
s 137
8.6%
n 91
 
5.7%
r 86
 
5.4%
83
 
5.2%
o 80
 
5.0%
u 75
 
4.7%
Other values (23) 213
13.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1587
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 330
20.8%
B 198
12.5%
e 151
9.5%
C 143
9.0%
s 137
8.6%
n 91
 
5.7%
r 86
 
5.4%
83
 
5.2%
o 80
 
5.0%
u 75
 
4.7%
Other values (23) 213
13.4%

cant_socios
Categorical

Missing 

Distinct5
Distinct (%)2.8%
Missing55
Missing (%)23.8%
Memory size15.1 KiB
2.0
79 
1.0
68 
3.0
17 
4.0
 
7
5.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters528
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row5.0
3rd row5.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 79
34.2%
1.0 68
29.4%
3.0 17
 
7.4%
4.0 7
 
3.0%
5.0 5
 
2.2%
(Missing) 55
23.8%

Length

2025-06-30T12:01:43.939060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T12:01:43.985930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 79
44.9%
1.0 68
38.6%
3.0 17
 
9.7%
4.0 7
 
4.0%
5.0 5
 
2.8%

Most occurring characters

ValueCountFrequency (%)
. 176
33.3%
0 176
33.3%
2 79
15.0%
1 68
 
12.9%
3 17
 
3.2%
4 7
 
1.3%
5 5
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 528
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 176
33.3%
0 176
33.3%
2 79
15.0%
1 68
 
12.9%
3 17
 
3.2%
4 7
 
1.3%
5 5
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 528
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 176
33.3%
0 176
33.3%
2 79
15.0%
1 68
 
12.9%
3 17
 
3.2%
4 7
 
1.3%
5 5
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 528
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 176
33.3%
0 176
33.3%
2 79
15.0%
1 68
 
12.9%
3 17
 
3.2%
4 7
 
1.3%
5 5
 
0.9%

cant_apercibimientos
Categorical

High correlation  Imbalance  Missing 

Distinct3
Distinct (%)2.1%
Missing91
Missing (%)39.4%
Memory size15.0 KiB
1.0
130 
2.0
 
9
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters420
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.7%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 130
56.3%
2.0 9
 
3.9%
3.0 1
 
0.4%
(Missing) 91
39.4%

Length

2025-06-30T12:01:44.064046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T12:01:44.110919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 130
92.9%
2.0 9
 
6.4%
3.0 1
 
0.7%

Most occurring characters

ValueCountFrequency (%)
. 140
33.3%
0 140
33.3%
1 130
31.0%
2 9
 
2.1%
3 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 420
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 140
33.3%
0 140
33.3%
1 130
31.0%
2 9
 
2.1%
3 1
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 420
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 140
33.3%
0 140
33.3%
1 130
31.0%
2 9
 
2.1%
3 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 420
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 140
33.3%
0 140
33.3%
1 130
31.0%
2 9
 
2.1%
3 1
 
0.2%

cant_suspensiones
Real number (ℝ)

High correlation  Missing 

Distinct7
Distinct (%)6.1%
Missing117
Missing (%)50.6%
Infinite0
Infinite (%)0.0%
Mean1.8333333
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2025-06-30T12:01:44.158372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile4
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1124132
Coefficient of variation (CV)0.60677084
Kurtosis6.4372696
Mean1.8333333
Median Absolute Deviation (MAD)1
Skewness2.2604151
Sum209
Variance1.2374631
MonotonicityNot monotonic
2025-06-30T12:01:44.205240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 51
22.1%
2 48
20.8%
3 6
 
2.6%
4 5
 
2.2%
6 2
 
0.9%
5 1
 
0.4%
7 1
 
0.4%
(Missing) 117
50.6%
ValueCountFrequency (%)
1 51
22.1%
2 48
20.8%
3 6
 
2.6%
4 5
 
2.2%
5 1
 
0.4%
6 2
 
0.9%
7 1
 
0.4%
ValueCountFrequency (%)
7 1
 
0.4%
6 2
 
0.9%
5 1
 
0.4%
4 5
 
2.2%
3 6
 
2.6%
2 48
20.8%
1 51
22.1%

cant_antecedentes
Real number (ℝ)

High correlation  Missing 

Distinct8
Distinct (%)3.6%
Missing7
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean1.7276786
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2025-06-30T12:01:44.267733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1644749
Coefficient of variation (CV)0.6740113
Kurtosis7.1487526
Mean1.7276786
Median Absolute Deviation (MAD)0
Skewness2.4200953
Sum387
Variance1.3560018
MonotonicityNot monotonic
2025-06-30T12:01:44.330226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 127
55.0%
2 65
28.1%
3 15
 
6.5%
4 8
 
3.5%
5 4
 
1.7%
6 3
 
1.3%
7 1
 
0.4%
8 1
 
0.4%
(Missing) 7
 
3.0%
ValueCountFrequency (%)
1 127
55.0%
2 65
28.1%
3 15
 
6.5%
4 8
 
3.5%
5 4
 
1.7%
6 3
 
1.3%
7 1
 
0.4%
8 1
 
0.4%
ValueCountFrequency (%)
8 1
 
0.4%
7 1
 
0.4%
6 3
 
1.3%
5 4
 
1.7%
4 8
 
3.5%
3 15
 
6.5%
2 65
28.1%
1 127
55.0%

cant_Apoderado
Categorical

High correlation  Imbalance  Missing 

Distinct5
Distinct (%)3.0%
Missing66
Missing (%)28.6%
Memory size15.1 KiB
1.0
136 
2.0
20 
3.0
 
5
4.0
 
3
5.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters495
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 136
58.9%
2.0 20
 
8.7%
3.0 5
 
2.2%
4.0 3
 
1.3%
5.0 1
 
0.4%
(Missing) 66
28.6%

Length

2025-06-30T12:01:44.392721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T12:01:44.455808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 136
82.4%
2.0 20
 
12.1%
3.0 5
 
3.0%
4.0 3
 
1.8%
5.0 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
. 165
33.3%
0 165
33.3%
1 136
27.5%
2 20
 
4.0%
3 5
 
1.0%
4 3
 
0.6%
5 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 495
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 165
33.3%
0 165
33.3%
1 136
27.5%
2 20
 
4.0%
3 5
 
1.0%
4 3
 
0.6%
5 1
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 495
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 165
33.3%
0 165
33.3%
1 136
27.5%
2 20
 
4.0%
3 5
 
1.0%
4 3
 
0.6%
5 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 495
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 165
33.3%
0 165
33.3%
1 136
27.5%
2 20
 
4.0%
3 5
 
1.0%
4 3
 
0.6%
5 1
 
0.2%

cant_representante
Categorical

High correlation  Imbalance  Missing 

Distinct3
Distinct (%)2.7%
Missing120
Missing (%)51.9%
Memory size14.9 KiB
1.0
96 
2.0
14 
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters333
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.9%

Sample

1st row1.0
2nd row2.0
3rd row2.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 96
41.6%
2.0 14
 
6.1%
3.0 1
 
0.4%
(Missing) 120
51.9%

Length

2025-06-30T12:01:44.518386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T12:01:44.565242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 96
86.5%
2.0 14
 
12.6%
3.0 1
 
0.9%

Most occurring characters

ValueCountFrequency (%)
. 111
33.3%
0 111
33.3%
1 96
28.8%
2 14
 
4.2%
3 1
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 333
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 111
33.3%
0 111
33.3%
1 96
28.8%
2 14
 
4.2%
3 1
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 333
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 111
33.3%
0 111
33.3%
1 96
28.8%
2 14
 
4.2%
3 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 333
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 111
33.3%
0 111
33.3%
1 96
28.8%
2 14
 
4.2%
3 1
 
0.3%

cant_autenticado
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
1.0
192 
2.0
34 
3.0
 
3
4.0
 
1
5.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters693
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.9%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 192
83.1%
2.0 34
 
14.7%
3.0 3
 
1.3%
4.0 1
 
0.4%
5.0 1
 
0.4%

Length

2025-06-30T12:01:44.613389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T12:01:44.675956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 192
83.1%
2.0 34
 
14.7%
3.0 3
 
1.3%
4.0 1
 
0.4%
5.0 1
 
0.4%

Most occurring characters

ValueCountFrequency (%)
. 231
33.3%
0 231
33.3%
1 192
27.7%
2 34
 
4.9%
3 3
 
0.4%
4 1
 
0.1%
5 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 693
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 231
33.3%
0 231
33.3%
1 192
27.7%
2 34
 
4.9%
3 3
 
0.4%
4 1
 
0.1%
5 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 693
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 231
33.3%
0 231
33.3%
1 192
27.7%
2 34
 
4.9%
3 3
 
0.4%
4 1
 
0.1%
5 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 693
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 231
33.3%
0 231
33.3%
1 192
27.7%
2 34
 
4.9%
3 3
 
0.4%
4 1
 
0.1%
5 1
 
0.1%

cant_noAutenticado
Categorical

High correlation  Imbalance  Missing 

Distinct4
Distinct (%)8.9%
Missing186
Missing (%)80.5%
Memory size14.6 KiB
1.0
37 
2.0
3.0
 
2
4.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters135
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)2.2%

Sample

1st row1.0
2nd row2.0
3rd row2.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 37
 
16.0%
2.0 5
 
2.2%
3.0 2
 
0.9%
4.0 1
 
0.4%
(Missing) 186
80.5%

Length

2025-06-30T12:01:44.738446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T12:01:44.785316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 37
82.2%
2.0 5
 
11.1%
3.0 2
 
4.4%
4.0 1
 
2.2%

Most occurring characters

ValueCountFrequency (%)
. 45
33.3%
0 45
33.3%
1 37
27.4%
2 5
 
3.7%
3 2
 
1.5%
4 1
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 135
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 45
33.3%
0 45
33.3%
1 37
27.4%
2 5
 
3.7%
3 2
 
1.5%
4 1
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 135
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 45
33.3%
0 45
33.3%
1 37
27.4%
2 5
 
3.7%
3 2
 
1.5%
4 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 135
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 45
33.3%
0 45
33.3%
1 37
27.4%
2 5
 
3.7%
3 2
 
1.5%
4 1
 
0.7%

cant_sinMontoLimite
Categorical

High correlation 

Distinct5
Distinct (%)2.2%
Missing1
Missing (%)0.4%
Memory size15.3 KiB
1.0
158 
2.0
57 
3.0
 
7
4.0
 
5
5.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters690
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row1.0
3rd row1.0
4th row2.0
5th row4.0

Common Values

ValueCountFrequency (%)
1.0 158
68.4%
2.0 57
 
24.7%
3.0 7
 
3.0%
4.0 5
 
2.2%
5.0 3
 
1.3%
(Missing) 1
 
0.4%

Length

2025-06-30T12:01:44.832194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T12:01:44.894612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 158
68.7%
2.0 57
 
24.8%
3.0 7
 
3.0%
4.0 5
 
2.2%
5.0 3
 
1.3%

Most occurring characters

ValueCountFrequency (%)
. 230
33.3%
0 230
33.3%
1 158
22.9%
2 57
 
8.3%
3 7
 
1.0%
4 5
 
0.7%
5 3
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 690
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 230
33.3%
0 230
33.3%
1 158
22.9%
2 57
 
8.3%
3 7
 
1.0%
4 5
 
0.7%
5 3
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 690
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 230
33.3%
0 230
33.3%
1 158
22.9%
2 57
 
8.3%
3 7
 
1.0%
4 5
 
0.7%
5 3
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 690
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 230
33.3%
0 230
33.3%
1 158
22.9%
2 57
 
8.3%
3 7
 
1.0%
4 5
 
0.7%
5 3
 
0.4%

cant_MontoLimite
Categorical

High correlation  Missing 

Distinct2
Distinct (%)33.3%
Missing225
Missing (%)97.4%
Memory size14.5 KiB
1.0
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)16.7%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 5
 
2.2%
2.0 1
 
0.4%
(Missing) 225
97.4%

Length

2025-06-30T12:01:44.957178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T12:01:45.004053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 5
83.3%
2.0 1
 
16.7%

Most occurring characters

ValueCountFrequency (%)
. 6
33.3%
0 6
33.3%
1 5
27.8%
2 1
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 6
33.3%
0 6
33.3%
1 5
27.8%
2 1
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 6
33.3%
0 6
33.3%
1 5
27.8%
2 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 6
33.3%
0 6
33.3%
1 5
27.8%
2 1
 
5.6%

total_articulos_provee
Real number (ℝ)

High correlation 

Distinct163
Distinct (%)70.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean461.66667
Minimum1
Maximum6993
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2025-06-30T12:01:45.066536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.5
Q125
median76
Q3304.5
95-th percentile3159.5
Maximum6993
Range6992
Interquartile range (IQR)279.5

Descriptive statistics

Standard deviation1140.3776
Coefficient of variation (CV)2.4701321
Kurtosis15.386786
Mean461.66667
Median Absolute Deviation (MAD)67
Skewness3.8669714
Sum106645
Variance1300461.2
MonotonicityNot monotonic
2025-06-30T12:01:45.161028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 11
 
4.8%
5 6
 
2.6%
31 5
 
2.2%
47 4
 
1.7%
3 4
 
1.7%
30 4
 
1.7%
105 3
 
1.3%
9 3
 
1.3%
37 3
 
1.3%
46 3
 
1.3%
Other values (153) 185
80.1%
ValueCountFrequency (%)
1 11
4.8%
2 1
 
0.4%
3 4
 
1.7%
4 3
 
1.3%
5 6
2.6%
6 3
 
1.3%
7 2
 
0.9%
8 1
 
0.4%
9 3
 
1.3%
10 1
 
0.4%
ValueCountFrequency (%)
6993 1
0.4%
6661 1
0.4%
6064 1
0.4%
5765 1
0.4%
5612 1
0.4%
4867 1
0.4%
4471 1
0.4%
3956 1
0.4%
3686 1
0.4%
3605 1
0.4%

dmonto_total_adjudicado
Categorical

High correlation 

Distinct21
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size20.0 KiB
(222.964.579- 46.172.150.151]
47 
(89.439.449- 222.964.579]
33 
(46.718.747- 89.439.449]
28 
(19.975.532- 30.451.916]
20 
(6.702.697- 9.424.898]
14 
Other values (16)
89 

Length

Max length29
Median length24
Mean length23.87013
Min length3

Characters and Unicode

Total characters5514
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(6.702.697- 9.424.898]
2nd row(19.975.532- 30.451.916]
3rd row(222.964.579- 46.172.150.151]
4th row(89.439.449- 222.964.579]
5th row(222.964.579- 46.172.150.151]

Common Values

ValueCountFrequency (%)
(222.964.579- 46.172.150.151] 47
20.3%
(89.439.449- 222.964.579] 33
14.3%
(46.718.747- 89.439.449] 28
12.1%
(19.975.532- 30.451.916] 20
8.7%
(6.702.697- 9.424.898] 14
 
6.1%
(13.557.176- 19.975.532] 12
 
5.2%
(2.483.085- 3.396.600] 9
 
3.9%
(9.424.898- 13.557.176] 9
 
3.9%
(4.727.330- 6.702.697] 8
 
3.5%
(30.451.916- 46.718.747] 8
 
3.5%
Other values (11) 43
18.6%

Length

2025-06-30T12:01:45.254733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
222.964.579 80
17.4%
89.439.449 61
13.3%
46.172.150.151 47
10.2%
46.718.747 36
 
7.8%
19.975.532 32
 
7.0%
30.451.916 28
 
6.1%
9.424.898 23
 
5.0%
6.702.697 22
 
4.8%
13.557.176 21
 
4.6%
2.483.085 15
 
3.3%
Other values (12) 95
20.7%

Most occurring characters

ValueCountFrequency (%)
. 917
16.6%
9 567
10.3%
4 496
9.0%
7 450
8.2%
2 423
 
7.7%
1 390
 
7.1%
5 354
 
6.4%
6 320
 
5.8%
3 266
 
4.8%
- 231
 
4.2%
Other values (7) 1100
19.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5514
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 917
16.6%
9 567
10.3%
4 496
9.0%
7 450
8.2%
2 423
 
7.7%
1 390
 
7.1%
5 354
 
6.4%
6 320
 
5.8%
3 266
 
4.8%
- 231
 
4.2%
Other values (7) 1100
19.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5514
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 917
16.6%
9 567
10.3%
4 496
9.0%
7 450
8.2%
2 423
 
7.7%
1 390
 
7.1%
5 354
 
6.4%
6 320
 
5.8%
3 266
 
4.8%
- 231
 
4.2%
Other values (7) 1100
19.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5514
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 917
16.6%
9 567
10.3%
4 496
9.0%
7 450
8.2%
2 423
 
7.7%
1 390
 
7.1%
5 354
 
6.4%
6 320
 
5.8%
3 266
 
4.8%
- 231
 
4.2%
Other values (7) 1100
19.9%

dcant_procesos_adjudicado
Categorical

High correlation 

Distinct10
Distinct (%)4.4%
Missing2
Missing (%)0.9%
Memory size17.4 KiB
(39.0, 1214.0]
98 
(19.0, 39.0]
35 
(12.0, 19.0]
23 
(0.999, 2.0]
21 
(2.0, 3.0]
18 
Other values (5)
34 

Length

Max length14
Median length12
Mean length12.441048
Min length10

Characters and Unicode

Total characters2849
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(19.0, 39.0]
2nd row(39.0, 1214.0]
3rd row(39.0, 1214.0]
4th row(39.0, 1214.0]
5th row(39.0, 1214.0]

Common Values

ValueCountFrequency (%)
(39.0, 1214.0] 98
42.4%
(19.0, 39.0] 35
 
15.2%
(12.0, 19.0] 23
 
10.0%
(0.999, 2.0] 21
 
9.1%
(2.0, 3.0] 18
 
7.8%
(8.0, 12.0] 9
 
3.9%
(6.0, 8.0] 8
 
3.5%
(4.0, 5.0] 7
 
3.0%
(5.0, 6.0] 5
 
2.2%
(3.0, 4.0] 5
 
2.2%
(Missing) 2
 
0.9%

Length

2025-06-30T12:01:45.332916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T12:01:45.411036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
39.0 133
29.0%
1214.0 98
21.4%
19.0 58
12.7%
2.0 39
 
8.5%
12.0 32
 
7.0%
3.0 23
 
5.0%
0.999 21
 
4.6%
8.0 17
 
3.7%
6.0 13
 
2.8%
4.0 12
 
2.6%

Most occurring characters

ValueCountFrequency (%)
0 458
16.1%
. 458
16.1%
1 286
10.0%
9 254
8.9%
, 229
8.0%
( 229
8.0%
] 229
8.0%
229
8.0%
2 169
 
5.9%
3 156
 
5.5%
Other values (4) 152
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2849
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 458
16.1%
. 458
16.1%
1 286
10.0%
9 254
8.9%
, 229
8.0%
( 229
8.0%
] 229
8.0%
229
8.0%
2 169
 
5.9%
3 156
 
5.5%
Other values (4) 152
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2849
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 458
16.1%
. 458
16.1%
1 286
10.0%
9 254
8.9%
, 229
8.0%
( 229
8.0%
] 229
8.0%
229
8.0%
2 169
 
5.9%
3 156
 
5.5%
Other values (4) 152
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2849
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 458
16.1%
. 458
16.1%
1 286
10.0%
9 254
8.9%
, 229
8.0%
( 229
8.0%
] 229
8.0%
229
8.0%
2 169
 
5.9%
3 156
 
5.5%
Other values (4) 152
 
5.3%

dtotal_articulos_provee
Categorical

High correlation 

Distinct15
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
(345.0, 6993.0]
53 
(97.6, 161.0]
26 
(58.0, 97.6]
25 
(161.0, 345.0]
24 
(40.0, 58.0]
22 
Other values (10)
81 

Length

Max length15
Median length14
Mean length12.822511
Min length10

Characters and Unicode

Total characters2962
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(97.6, 161.0]
2nd row(97.6, 161.0]
3rd row(21.0, 29.0]
4th row(161.0, 345.0]
5th row(161.0, 345.0]

Common Values

ValueCountFrequency (%)
(345.0, 6993.0] 53
22.9%
(97.6, 161.0] 26
11.3%
(58.0, 97.6] 25
10.8%
(161.0, 345.0] 24
10.4%
(40.0, 58.0] 22
9.5%
(29.0, 40.0] 16
 
6.9%
(0.999, 2.0] 12
 
5.2%
(21.0, 29.0] 11
 
4.8%
(15.0, 21.0] 10
 
4.3%
(4.0, 6.0] 9
 
3.9%
Other values (5) 23
10.0%

Length

2025-06-30T12:01:45.505413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
345.0 77
16.7%
6993.0 53
11.5%
97.6 51
11.0%
161.0 50
10.8%
58.0 47
10.2%
40.0 38
8.2%
29.0 27
 
5.8%
21.0 21
 
4.5%
15.0 18
 
3.9%
2.0 16
 
3.5%
Other values (6) 64
13.9%

Most occurring characters

ValueCountFrequency (%)
. 462
15.6%
0 449
15.2%
( 231
7.8%
, 231
7.8%
231
7.8%
] 231
7.8%
9 220
7.4%
6 166
 
5.6%
1 165
 
5.6%
5 142
 
4.8%
Other values (5) 434
14.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2962
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 462
15.6%
0 449
15.2%
( 231
7.8%
, 231
7.8%
231
7.8%
] 231
7.8%
9 220
7.4%
6 166
 
5.6%
1 165
 
5.6%
5 142
 
4.8%
Other values (5) 434
14.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2962
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 462
15.6%
0 449
15.2%
( 231
7.8%
, 231
7.8%
231
7.8%
] 231
7.8%
9 220
7.4%
6 166
 
5.6%
1 165
 
5.6%
5 142
 
4.8%
Other values (5) 434
14.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2962
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 462
15.6%
0 449
15.2%
( 231
7.8%
, 231
7.8%
231
7.8%
] 231
7.8%
9 220
7.4%
6 166
 
5.6%
1 165
 
5.6%
5 142
 
4.8%
Other values (5) 434
14.7%

Cluster_6
Categorical

Constant 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size14.9 KiB
1
231 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters231
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 231
100.0%

Length

2025-06-30T12:01:45.567904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T12:01:45.599852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 231
100.0%

Most occurring characters

ValueCountFrequency (%)
1 231
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 231
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 231
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 231
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 231
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 231
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 231
100.0%

Interactions

2025-06-30T12:01:40.383364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T12:01:36.355160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T12:01:37.096625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T12:01:37.566815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T12:01:38.098294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T12:01:38.630882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T12:01:39.131304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T12:01:39.804592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T12:01:40.477662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T12:01:36.434029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T12:01:37.159199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T12:01:37.645002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T12:01:38.160818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T12:01:38.693306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T12:01:39.225053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-30T12:01:38.802670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T12:01:39.350111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T12:01:40.007696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T12:01:40.699892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T12:01:36.641717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T12:01:37.346686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-30T12:01:40.762382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T12:01:36.719837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T12:01:37.393548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T12:01:37.894971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T12:01:38.426992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T12:01:38.927723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T12:01:39.475803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-30T12:01:37.456795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T12:01:37.957431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T12:01:38.489558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T12:01:39.005850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-30T12:01:40.227134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-30T12:01:38.019958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T12:01:38.568407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T12:01:39.068266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-30T12:01:40.305248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-30T12:01:45.663043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
EstadoTipoSocietarioanio_preinscripcionantiguedadcant_Apoderadocant_MontoLimitecant_antecedentescant_apercibimientoscant_autenticadocant_noAutenticadocant_procesos_adjudicadocant_representantecant_sinMontoLimitecant_socioscant_suspensionesdcant_procesos_adjudicadodmonto_total_adjudicadodtotal_articulos_proveemonto_total_adjudicadoperiodo_preinscripcionprovinciatotal_articulos_provee
Estado1.0000.3350.0910.1270.0001.0000.0000.0000.1670.0000.0000.0000.0000.0000.0000.1410.3830.0710.0000.1640.3700.000
TipoSocietario0.3351.0000.1820.2140.0000.0000.0000.0000.0000.0000.0000.0000.0960.2300.0000.0620.2900.2270.4260.1900.3530.000
anio_preinscripcion0.0910.1821.000-1.0000.0000.0000.0680.0000.0000.000-0.2050.0000.0000.0000.0710.1740.1820.000-0.2390.9320.199-0.030
antiguedad0.1270.214-1.0001.0000.0000.000-0.0680.0000.0000.0000.2050.0000.0000.000-0.0710.1390.1790.0000.239-0.9320.2390.030
cant_Apoderado0.0000.0000.0000.0001.0000.8160.0000.0000.4870.6080.0000.4340.5830.0000.0000.0000.0000.2180.0000.0000.0000.000
cant_MontoLimite1.0000.0000.0000.0000.8161.0000.8661.0000.0000.0000.0001.0000.0000.0001.0000.7070.7070.0001.0000.0000.0001.000
cant_antecedentes0.0000.0000.068-0.0680.0000.8661.0000.3330.0520.0000.0030.2420.0000.0000.7640.0410.0000.000-0.1310.0820.2720.192
cant_apercibimientos0.0000.0000.0000.0000.0001.0000.3331.0000.0001.0000.0000.0000.0000.0000.0000.2820.1830.0000.0000.0000.0000.116
cant_autenticado0.1670.0000.0000.0000.4870.0000.0520.0001.0000.0000.0000.5150.5500.0010.0000.0000.0000.0000.0000.1530.1930.000
cant_noAutenticado0.0000.0000.0000.0000.6080.0000.0001.0000.0001.0000.0000.0000.5550.1490.0000.0000.2930.6350.0000.0000.0000.000
cant_procesos_adjudicado0.0000.000-0.2050.2050.0000.0000.0030.0000.0000.0001.0000.0000.0000.072-0.2200.0000.0000.0000.652-0.2210.0000.378
cant_representante0.0000.0000.0000.0000.4341.0000.2420.0000.5150.0000.0001.0000.4080.1640.4000.2520.4300.0000.0930.0000.0000.148
cant_sinMontoLimite0.0000.0960.0000.0000.5830.0000.0000.0000.5500.5550.0000.4081.0000.0000.0000.0000.0000.1320.1010.0700.0000.000
cant_socios0.0000.2300.0000.0000.0000.0000.0000.0000.0010.1490.0720.1640.0001.0000.0000.1020.0000.0750.2030.0000.1940.000
cant_suspensiones0.0000.0000.071-0.0710.0001.0000.7640.0000.0000.000-0.2200.4000.0000.0001.0000.0990.1520.000-0.2950.0830.3320.162
dcant_procesos_adjudicado0.1410.0620.1740.1390.0000.7070.0410.2820.0000.0000.0000.2520.0000.1020.0991.0000.3150.1510.0000.1500.1020.000
dmonto_total_adjudicado0.3830.2900.1820.1790.0000.7070.0000.1830.0000.2930.0000.4300.0000.0000.1520.3151.0000.0370.0000.1670.2250.000
dtotal_articulos_provee0.0710.2270.0000.0000.2180.0000.0000.0000.0000.6350.0000.0000.1320.0750.0000.1510.0371.0000.2140.0000.1490.043
monto_total_adjudicado0.0000.426-0.2390.2390.0001.000-0.1310.0000.0000.0000.6520.0930.1010.203-0.2950.0000.0000.2141.000-0.2700.0000.115
periodo_preinscripcion0.1640.1900.932-0.9320.0000.0000.0820.0000.1530.000-0.2210.0000.0700.0000.0830.1500.1670.000-0.2701.0000.306-0.030
provincia0.3700.3530.1990.2390.0000.0000.2720.0000.1930.0000.0000.0000.0000.1940.3320.1020.2250.1490.0000.3061.0000.000
total_articulos_provee0.0000.000-0.0300.0300.0001.0000.1920.1160.0000.0000.3780.1480.0000.0000.1620.0000.0000.0430.115-0.0300.0001.000

Missing values

2025-06-30T12:01:41.027978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-30T12:01:41.200676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-06-30T12:01:41.388233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CUITNombreFechaPreinscripcionEstadoTipoSocietarioperiodo_preinscripcionanio_preinscripcioncant_procesos_adjudicadomonto_total_adjudicadoantiguedadprovinciacant_socioscant_apercibimientoscant_suspensionescant_antecedentescant_Apoderadocant_representantecant_autenticadocant_noAutenticadocant_sinMontoLimitecant_MontoLimitetotal_articulos_proveedmonto_total_adjudicadodcant_procesos_adjudicadodtotal_articulos_proveeCluster_6
230711500363LICICOM S.R.L.15/09/2016InscriptoS.R.L201609201622.07.425067e+065.0CABA2.01.02.04.02.01.02.01.03.0NaN105.0(6.702.697- 9.424.898](19.0, 39.0](97.6, 161.0]1
2330590151013VIDITEC S.A..22/07/2016InscriptoSociedad Anónima201607201647.02.714623e+075.0CABA5.01.0NaN1.01.0NaN1.0NaN1.0NaN113.0(19.975.532- 30.451.916](39.0, 1214.0](97.6, 161.0]1
2430678561165NACION SEGUROS S.A.15/11/2016InscriptoSociedad Anónima20161120161102.06.933819e+095.0CABA5.0NaNNaNNaN1.0NaN1.0NaN1.0NaN26.0(222.964.579- 46.172.150.151](39.0, 1214.0](21.0, 29.0]1
2930591267759ERRE-DE SRL02/08/2016InscriptoS.R.L2016082016340.01.379889e+085.0Buenos Aires2.01.0NaN1.03.0NaN1.02.02.01.0226.0(89.439.449- 222.964.579](39.0, 1214.0](161.0, 345.0]1
3530702024834Datastar Argentina S.A.09/09/2016InscriptoSociedad Anónima2016092016111.03.182254e+095.0CABA2.01.0NaN1.02.02.02.02.04.0NaN207.0(222.964.579- 46.172.150.151](39.0, 1214.0](161.0, 345.0]1
3930623295946LAVIERI HNOS DE LAVIERI SEBASTIAN GABRIEL LAVIERI ALEJANDRO CARLOS.29/09/2016InscriptoSociedades De Hecho201609201622.07.831100e+065.0CABA2.01.0NaN1.0NaN2.02.0NaN2.0NaN37.0(6.702.697- 9.424.898](19.0, 39.0](29.0, 40.0]1
6030673249902LOMAS DEL SOL SRL21/10/2016SuspendidoS.R.L201610201627.01.375720e+095.0San Juan2.0NaN4.04.01.0NaN1.0NaN1.0NaN602.0(222.964.579- 46.172.150.151](19.0, 39.0](345.0, 6993.0]1
8530714236888NANOTEC S.R.L.30/09/2016InscriptoS.R.L201609201612.07.242921e+065.0CABA2.01.0NaN1.01.0NaN1.0NaN1.0NaN160.0(6.702.697- 9.424.898](8.0, 12.0](97.6, 161.0]1
8930710362218Licenciasonline SA13/09/2016InscriptoSociedad Anónima20160920161.08.607014e+075.0CABA4.01.0NaN1.01.01.01.01.02.0NaN3.0(46.718.747- 89.439.449](0.999, 2.0](2.0, 3.0]1
10330710828608INFORMÁTICA PALMAR SRL04/10/2016InscriptoS.R.L2016102016143.06.447596e+075.0CABA2.01.0NaN1.0NaN2.01.01.02.0NaN37.0(46.718.747- 89.439.449](39.0, 1214.0](29.0, 40.0]1
CUITNombreFechaPreinscripcionEstadoTipoSocietarioperiodo_preinscripcionanio_preinscripcioncant_procesos_adjudicadomonto_total_adjudicadoantiguedadprovinciacant_socioscant_apercibimientoscant_suspensionescant_antecedentescant_Apoderadocant_representantecant_autenticadocant_noAutenticadocant_sinMontoLimitecant_MontoLimitetotal_articulos_proveedmonto_total_adjudicadodcant_procesos_adjudicadodtotal_articulos_proveeCluster_6
815030715995979DESARROLLOS URBANOS RIO DE LA PLATA SRL16/09/2020InscriptoS.R.L202009202018.02.095676e+081.0CABA2.01.0NaN1.0NaN1.01.0NaN1.0NaN18.0(89.439.449- 222.964.579](12.0, 19.0](15.0, 21.0]1
816530709585831SECON SECURITY CONCEPT SA11/04/2017InscriptoSociedad Anónima20170420171.01.326591e+074.0Buenos Aires2.01.0NaN1.01.01.01.01.01.01.0102.0(9.424.898- 13.557.176](0.999, 2.0](97.6, 161.0]1
819320402312514Lionel Druscovich12/11/2021InscriptoPersona Física20211120214.01.198617e+060.0CABANaNNaNNaNNaN1.0NaN1.0NaN1.0NaN4471.0(890.758- 1.302.657](3.0, 4.0](345.0, 6993.0]1
866830701547531ASTILLEROS PATAGONICOS INTEGRADOS16/08/2018InscriptoSociedad Anónima20180820182.05.357829e+073.0Buenos Aires1.0NaN1.01.01.01.01.01.02.0NaN17.0(46.718.747- 89.439.449](0.999, 2.0](15.0, 21.0]1
895627181287077FABIANA SANDRA CORTES10/04/2017InscriptoPersona Física201704201713.01.682270e+074.0Buenos AiresNaN1.07.08.01.0NaN1.0NaN1.0NaN254.0(13.557.176- 19.975.532](12.0, 19.0](161.0, 345.0]1
8957A28006104AIRBUS DEFENCE AND SPACE S.A.21/06/2019Pre InscriptoPJ Extranjero Sin Sucursal20190620193.08.494348e+042.0Extranjera1.01.0NaN1.02.0NaN2.0NaN2.0NaN14.0(33.011- 104.767](2.0, 3.0](11.0, 15.0]1
9237214349010016FARINTO S.A.05/07/2019Pre InscriptoPJ Extranjero Sin Sucursal2019072019NaNNaN2.0Extranjera1.01.0NaN1.01.0NaN1.0NaN1.0NaN6.0nanNaN(4.0, 6.0]1
950430716582082BATERIAS ECOBAT S.A.S16/10/2020InscriptoOtras Formas Societarias20201020201.08.288520e+051.0San Juan1.01.0NaN1.0NaN1.01.0NaN1.0NaN1.0(599.760- 890.758](0.999, 2.0](0.999, 2.0]1
971620365997005Alejandro Javier thea20/02/2022InscriptoPersona Física202202202219.06.293224e+060.0Buenos AiresNaNNaNNaNNaN1.0NaN1.0NaN1.0NaN5612.0(4.727.330- 6.702.697](12.0, 19.0](345.0, 6993.0]1
978330707835563SUTEL S.R.L.25/08/2016InscriptoS.R.L20160820162.03.889114e+065.0Buenos Aires2.0NaN2.02.0NaN1.01.0NaN1.0NaN329.0(3.396.600- 4.727.330](0.999, 2.0](161.0, 345.0]1